# Time Series in 5-Minutes, Part 1: Visualization with the Time Plot

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**Have 5-minutes? Then let’s learn time series.** In this short articles series, I highlight how you can get up to speed quickly on important aspects of time series analysis. Today we are focusing on the most fundamental tool, **the time plot.** Learn how to make *interactive* (`plotly`

) and *static* (`ggplot2`

) visualizations easily with `timetk`

.

### Updates

This article has been updated. View the updated Time Series in 5-Minutes article at Business Science.

## Time Series in 5-Mintues

Articles in this Series

I just released `timetk`

2.0.0 (read the release announcement). A ton of new functionality has been added. We’ll discuss some of the key pieces in this article series:

- Part 1, The Time Plot
- Part 2, Autocorrelation
- Part 3, Seasonality
- Part 4, Anomalies and Anomaly Detection
- Part 5, Dealing with Missing Time Series Data

???? **Register for our blog to get new articles as we release them.**

# Have 5-Minutes?

Then let’s learn the Time Plot

This tutorial focuses on, `plot_time_series()`

, a workhorse time-series plotting function that:

- Generates interactive
`plotly`

plots (great for exploring & shiny apps) - Consolidates 20+ lines of
`ggplot2`

&`plotly`

code - Scales well to many time series
- Can be converted from interactive
`plotly`

to static`ggplot2`

plots

Load the following libraries. For the purposes of this tutorial, I’m setting all plots to static `ggplot2`

using `interactive <- FALSE`

, but I encourage you to switch this to `TRUE`

to see how easy it is to make interactive `plotly`

plots.

## Plotting a Single Time Series

Let’s start with a popular time series, `taylor_30_min`

, which includes energy demand in megawatts at a sampling interval of 30-minutes. This is a single time series.

The `plot_time_series()`

function generates an interactive `plotly`

chart by default.

- Simply provide the date variable (time-based column,
`.date_var`

) and the numeric variable (`.value`

) that changes over time as the first 2 arguments - When
`.interactive = TRUE`

, the`.plotly_slider = TRUE`

adds a date slider to the bottom of the chart.

## Plotting Groups

Next, let’s move on to a dataset with time series groups, `m4_daily`

, which is a sample of 4 time series from the M4 competition that are sampled at a daily frequency.

**Visualizing grouped data** is as simple as grouping the data set with `group_by()`

prior to piping into the `plot_time_series()`

function. Key points:

- Groups can be added in 2 ways: by
`group_by()`

or by using the`...`

to add groups. - Groups are then converted to facets.
`.facet_ncol = 2`

returns a 2-column faceted plot`.facet_scales = "free"`

allows the x and y-axis of each plot to scale independently of the other plots

## Visualizing Trend with the Smoother

You may be wondering, what is that ** blue line** that keeps showing up on all of our plots. It’s called a

**smoother**, and it’s a really awesome way to visualize trend through the noise in a time series.

We can adjust the smoother using:

**Toggle on/off:**`.smooth = TRUE/FALSE`

**Change the flexibility of the line:**Try`.smooth_period = "52 weeks"`

(30-days of data) or`.smooth_span = 0.25`

(25% of data). By default,`.smooth_span`

gets priority.

Here I’m changing the `smooth_span = 0.25`

to increase the flexibility by using 25% of the data in the smoother. Not that the time series I’m using is a weekly series, `m4_weekly`

.

## Visualizing Transformations & Sub-Groups

Let’s switch to an hourly dataset with multiple groups. We can showcase:

**Log transformation**to the`.value`

- Use of
`.color_var`

to highlight**sub-groups.**

The intent is to showcase the groups in faceted plots, but to highlight weekly windows (weekly sub-groups, using `week()`

) within the data while simultaneously doing a `log()`

transformation to the value. This is simple to do:

`.value = log(value)`

Applies the Log Transformation- The data is ungrouped, so we can add facets internally using the
`...`

to supply one or more facet columns. `.color_var = week(date)`

The date column is transformed to a`lubridate::week()`

number. The color is applied to each of the week numbers.

## Static ggplot2 Visualizations & Customizations

All of the visualizations can be converted from interactive `plotly`

(great for exploring and shiny apps) to static `ggplot2`

visualizations (great for reports).

- Toggle Interactive/Static:
`.interactive = TRUE/FALSE`

- Add title, legend, x & y-axis labels:
`.title`

,`.color_lab`

,`.x_lab`

and`.y_lab`

## Time Series Course (Coming Soon)

I teach Time Series (`timetk`

, more) in my **Time Series Analysis & Forecasting Course**. If interested in learning Pro-Forecasting Strategies then join my waitlist. The course is coming soon.

You will learn:

- Time Series Preprocessing, Noise Reduction, & Anomaly Detection
- Feature engineering using lagged variables & external regressors
- Hyperparameter tuning
- Time series cross-validation
- Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- NEW - Deep Learning with RNNs (Competition Winner)
- and more.

Signup for the Time Series Course waitlist

# Have questions on using Timetk for time series?

Make a comment in the chat below. ????

And, if you plan on using `timetk`

for your business, it’s a no-brainer - Join my Time Series Course Waitlist (It’s coming, it’s really insane).

**leave a comment**for the author, please follow the link and comment on their blog:

**business-science.io**.

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